Methods of incorporating common element characteristics for law article prediction

Law article prediction is a task of predicting the relevant laws and regulations involved in a case according to the description text of the case, and it has broad application prospects in improving judicial efficiency. In the existing research work, researchers often only consider a single case, employing the neural network method to extract features for prediction, which lack the mining of related and common element information between different data. In order to solve this problem, we propose a law article prediction method that integrates the characteristics of common elements. It can effectively utilize the co-occurrence information of the training data, fully mine the relevant common elements between cases, and fuse local features. Experiments show that our method performs well.

Automated summary

Law article prediction is a task of predicting relevant laws and regulations in a case based on its description text, with great potential in improving judicial efficiency. Current research often focuses on single cases, using neural network methods to extract features for prediction, neglecting the mining of related information between different data. To address this, we propose a method that integrates common element characteristics for law article prediction, effectively utilizing co-occurrence information to mine relevant common elements and fuse local features. Experimental results demonstrate the effectiveness of our method.